{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall / Near-Fall / ADL Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, Columns) in training dataset (480, 136)\n",
      "Number of Features in training dataset:- 136\n",
      "Number of Features in testing dataset:- 136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import ensemble\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Random values generated should be same everytime the program is run\n",
    "np.random.seed(7)\n",
    "\n",
    "#Reading of Training and Testing Data from CSV File\n",
    "data=pd.read_csv(\"Fall15Multi.csv\")\n",
    "train=data.iloc[0:480,:]\n",
    "test=data.iloc[480:,:]\n",
    "print(\"(Rows, Columns) in training dataset\",train.shape)\n",
    "print(\"Number of Features in training dataset:-\",train.shape[1])\n",
    "print(\"Number of Features in testing dataset:-\",test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "Replace NaN values with Minimum of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#Transforming Columns containg \"NaN\" Values\n",
    "#in both training and testing dataset\n",
    "for col in train.columns:\n",
    "    val = train[col].min()\n",
    "    train[col]=train[col].fillna(value=val,axis=0)\n",
    "\n",
    "for col in test.columns:\n",
    "    val = test[col].min()\n",
    "    test[col]=test[col].fillna(value=val,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Train and Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating training data Features and Predictions into different dataframes\n",
    "X_train=train.drop('class_label',axis=1)\n",
    "Y_train=train['class_label']\n",
    "\n",
    "#Separating testing data Features and Predictions into different dataframes\n",
    "X_test=test.drop('class_label',axis=1)\n",
    "Y_test=test['class_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a6cf6d5f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADGhJREFUeJzt3X+o3fV9x/Hnq6btH7PQSqKkMTbS\nZXT2j6Xu4gT/yRDmj39i/7AoowkiS/+ITJl/zBaG9g+hf6wdFDZZiq4pdDpZWwxDtrmsUspm9Spi\nTTNnaJ3eJiS3q7RKoVvie3/cb/As3txf557c5J3nAy7nnM/5fs95hwPP+/V7zzmmqpAk9fW+tR5A\nkjRZhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0tGvokm5N8N8mhJAeT3D2sP5Dkp0leHH5uHtnn\n80kOJ3klyQ2T/AdIkhaWxT4wlWQjsLGqXkjyIeB54BbgM8DbVfXnp21/FfAocA3wUeBfgN+qqpMT\nmF+StIh1i21QVUeBo8P1t5IcAjYtsMsO4LGq+jXwkySHmYv+v59ph/Xr19eWLVuWM7ckXfCef/75\nn1XVhsW2WzT0o5JsAT4F/AC4DrgryU5gGri3qt5k7pfAMyO7zbDwLwa2bNnC9PT0ckaRpAtekv9a\nynZL/mNskouBbwH3VNUvgYeAjwPbmDvi//KpTefZ/T3nh5LsTjKdZHp2dnapY0iSlmlJoU/yfuYi\n/82q+jZAVR2rqpNV9Q7wNeZOz8DcEfzmkd0vB46c/phVtbeqpqpqasOGRf/LQ5K0Qkt5102Ah4FD\nVfWVkfWNI5t9Gnh5uL4fuC3JB5NcCWwFnl29kSVJy7GUc/TXAZ8FfpjkxWHtC8DtSbYxd1rmNeBz\nAFV1MMnjwI+AE8Ae33EjSWtnKe+6+T7zn3d/coF9HgQeHGMuSdIq8ZOxktScoZek5gy9JDW3rA9M\ntZH5/uTQiP8fYEkjPKKXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn\n6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz\n9JLUnKGXpObWrfUA0nLli1nrESaq7q+1HkHNeEQvSc0ZeklqztBLUnOGXpKaM/SS1NyioU+yOcl3\nkxxKcjDJ3cP6JUmeSvLqcPmRYT1JvprkcJKXklw96X+EJOnMlnJEfwK4t6p+G7gW2JPkKuA+4EBV\nbQUODLcBbgK2Dj+7gYdWfWpJ0pItGvqqOlpVLwzX3wIOAZuAHcC+YbN9wC3D9R3AN2rOM8CHk2xc\n9cklSUuyrHP0SbYAnwJ+AFxWVUdh7pcBcOmw2SbgjZHdZoa10x9rd5LpJNOzs7PLn1yStCRLDn2S\ni4FvAfdU1S8X2nSetfd81K+q9lbVVFVNbdiwYaljSJKWaUmhT/J+5iL/zar69rB87NQpmeHy+LA+\nA2we2f1y4MjqjCtJWq6lvOsmwMPAoar6yshd+4Fdw/VdwBMj6zuHd99cC/zi1CkeSdLZt5QvNbsO\n+CzwwyQvDmtfAL4EPJ7kTuB14NbhvieBm4HDwK+AO1Z1YknSsiwa+qr6PvOfdwe4fp7tC9gz5lyS\npFXiJ2MlqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOG\nXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlD\nL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0tGvokjyQ5nuTlkbUHkvw0yYvD\nz80j930+yeEkryS5YVKDS5KWZilH9F8Hbpxn/S+qatvw8yRAkquA24BPDvv8VZKLVmtYSdLyLRr6\nqvoe8PMlPt4O4LGq+nVV/QQ4DFwzxnySpDGtG2Pfu5LsBKaBe6vqTWAT8MzINjPD2nsk2Q3sBrji\niivGGEPS+SJPP73WI0xUbd++1iPMa6V/jH0I+DiwDTgKfHlYzzzb1nwPUFV7q2qqqqY2bNiwwjEk\nSYtZUeir6lhVnayqd4Cv8e7pmRlg88imlwNHxhtRkjSOFYU+ycaRm58GTr0jZz9wW5IPJrkS2Ao8\nO96IkqRxLHqOPsmjwHZgfZIZ4H5ge5JtzJ2WeQ34HEBVHUzyOPAj4ASwp6pOTmZ0SdJSLBr6qrp9\nnuWHF9j+QeDBcYaSJK0ePxkrSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6\nSWrO0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9\nJDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWpu0dAneSTJ\n8SQvj6xdkuSpJK8Olx8Z1pPkq0kOJ3kpydWTHF6StLilHNF/HbjxtLX7gANVtRU4MNwGuAnYOvzs\nBh5anTElSSu1aOir6nvAz09b3gHsG67vA24ZWf9GzXkG+HCSjas1rCRp+VZ6jv6yqjoKMFxeOqxv\nAt4Y2W5mWHuPJLuTTCeZnp2dXeEYkqTFrPYfYzPPWs23YVXtraqpqprasGHDKo8hSTplpaE/duqU\nzHB5fFifATaPbHc5cGTl40mSxrXS0O8Hdg3XdwFPjKzvHN59cy3wi1OneCRJa2PdYhskeRTYDqxP\nMgPcD3wJeDzJncDrwK3D5k8CNwOHgV8Bd0xgZknSMiwa+qq6/Qx3XT/PtgXsGXcoSdLq8ZOxktSc\noZek5gy9JDVn6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO\n0EtSc4Zekpoz9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn\n6CWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6Tm1o2zc5LXgLeAk8CJqppKcgnwd8AW4DXgM1X1\n5nhjSpJWajWO6H+/qrZV1dRw+z7gQFVtBQ4MtyVJa2QSp252APuG6/uAWybwHJKkJRo39AX8c5Ln\nk+we1i6rqqMAw+Wl8+2YZHeS6STTs7OzY44hSTqTsc7RA9dV1ZEklwJPJfmPpe5YVXuBvQBTU1M1\n5hySpDMY64i+qo4Ml8eB7wDXAMeSbAQYLo+PO6QkaeVWHPokv5HkQ6euA38AvAzsB3YNm+0Cnhh3\nSEnSyo1z6uYy4DtJTj3O31bVPyZ5Dng8yZ3A68Ct448pSVqpFYe+qn4M/M486/8NXD/OUJKk1eMn\nYyWpOUMvSc0ZeklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzRl6SWrO0EtSc4Zekpoz\n9JLUnKGXpOYMvSQ1Z+glqTlDL0nNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDVn6CWpOUMvSc0Z\neklqztBLUnOGXpKaM/SS1Jyhl6TmDL0kNWfoJak5Qy9JzU0s9EluTPJKksNJ7pvU80iSFjaR0Ce5\nCPhL4CbgKuD2JFdN4rkkSQub1BH9NcDhqvpxVf0P8BiwY0LPJUlawLoJPe4m4I2R2zPA741ukGQ3\nsHu4+XaSVyY0y7lgPfCzs/ZsyVl7qgvEWX398oCv3yo6u6/d2Xqid31sKRtNKvTz/Xvr/92o2gvs\nndDzn1OSTFfV1FrPoZXx9Tt/+drNmdSpmxlg88jty4EjE3ouSdICJhX654CtSa5M8gHgNmD/hJ5L\nkrSAiZy6qaoTSe4C/gm4CHikqg5O4rnOExfEKarGfP3OX752QKpq8a0kSectPxkrSc0ZeklqztBL\nUnOTeh+9JJ11ST7B3KfwNzH32Z0jwP6qOrSmg60xj+gnIMknklyf5OLT1m9cq5mk7pL8KXNftxLg\nWebe5h3g0Qv9ixV9180qS/LHwB7gELANuLuqnhjue6Gqrl7L+bRySe6oqr9Z6zk0vyT/CXyyqv73\ntPUPAAerauvaTLb2PKJffX8E/G5V3QJsB/4syd3DfX6Jyfnti2s9gBb0DvDRedY3DvddsDxHv/ou\nqqq3AarqtSTbgb9P8jEM/TkvyUtnugu47GzOomW7BziQ5FXe/VLFK4DfBO5as6nOAZ66WWVJ/hX4\nk6p6cWRtHfAI8IdVddGaDadFJTkG3AC8efpdwL9V1XxHjDpHJHkfc1+Tvom512wGeK6qTq7pYGvM\nI/rVtxM4MbpQVSeAnUn+em1G0jL8A3Dx6C/qU5I8ffbH0XJU1TvAM2s9x7nGI3pJas4/xkpSc4Ze\nkpoz9JLUnKGXpOb+Dz3aviltvsQ0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a6cf60780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.class_label.value_counts().plot(kind='bar',colors='RGC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a6cf60fd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T1SdFZICIjFTVHfEKFiPbsMKd9CLiCx3qd15pRc7M48d6jRytvrRcIDdmxw/t\nfR/Crdpe72T4+JAXdz3c56qhn3srO3PAJbHKYJJaDbDf7RBt0drpgD/AmVUyFmeNkgyci2OS/R92\nKc6nBJNkwr7Mk3sHX7Rh97DLwie6DRnfmsHF9grVvr2XNuyLGtFw1j92P3HJ5H5Xl4zqef6l0V2g\nTOe1Kacw31PLpLa2xX0jcAHOGiKo6h4Rd3fvbqWNbgcwpwQyehyuHHZZmTO42MfflsHF9lJVjYR2\n/tuKgK2x5vArMw7U7Vpz8YCPnRO9ath0Tp6rE60t3AFVVRFRgOigpBeUuh0g1Z3sOqBiV84V2w8M\nnNQnlN5tfHsHF9srEtpRCtruNUl2nSibXBU4UH7VsDsOp0n6qJbfYTzIc3WitYX7ORF5DOgtIl8A\n7gR+H79YMeO5H0hnUNVr1HsVZxXsP9x3fMPgYrOzOeItVLuy2TXjW+tY8PCIv+185Ng1OXet7pbe\na0oscpmk4rk60ZZZJR8BrsKZVfKKqv4znsFipSw3rxQPbgbqJc7gon9jZc7MY9W9Ro1WX1qLezsm\ngmo4WF/10HGgb4svbp3IpYM+uWxYtzGXx+h4xn1BoE9OYf6Z1k9KOq0dnLxfVb8N/LOJx5LdEqxw\nx1zYl3ly36CppZXDLgud6D50HCKxmn8fM5HAe+uAWLaQfW/u/8vl5/aavHxi34KJItIthsc27ljh\ntaINre8q+QhwepG+tonHktES4Gtuh+gMAhk9Du8eml+2Z8i0zPouff2IXOR2pjMJ1a0OxuO47x9b\nM/1w/d73Cobc2ssnvqHxOIdJmCUtvyT5nLFwi8gXgS8Bo0RkQ6OnegJvxTNYDJXg7HdpU7ra4WTX\nARUVOQXb9w+c1DuU3r3dVy4mmmrwpEaOnB+v4x+u3537UsWjB68ddteGzLSutiGDd3mycJ+xjzs6\nBaoP8DPgvkZPHVfVI3HOFjNluXlvkvxzzpNGda+RW3blXLHvcL/xQyJpma2e/5xMQnVrlodql02P\n93kEX/CKobet6NdlqG2B5j3HgP45hflx+WQWT2dscUd3ca/GWRcbERkIZAE9RKSHqu6Kf8SYeA4r\n3M1SJHyov7+0Imfmsepeo0epL20szsVWnhWqezchO7YrkYzX9jydf36fy5eNzZ46TaTpjYpNUvqr\nF4s2tH5w8mPAr4ChOIuxnI2zke/4+EWLqQU4+a27JCrsy6jdN2hq6e5hlwVrug/LS8bBxfbSSO1R\n9HhCv5/1R5dedrC+ct2lAz9xloj0S+S5Tbv9ye0A7dWq6YAish4oAF5T1QtEZCZwS4zWzU6Isty8\nV3EGWVNWIKP7kd1D88v2DpkK0A64AAAWsElEQVSeUdel7wQ66ayI4Mk33gjXr3al66J7eu/Ka4bd\neSLdl+HpTywpYD8wLKcwP+x2kPZo7aySoKoeFhGfiPhU9XURuT+uyWLvWVKwcJ/s2r+yIqdg+4GB\nF2YHncHFTt9lFA6U9nLr3CdCVTkv7nr45NXDPv92z4y+09zKYVr0Z68WbWh94a6Kbqy6DPijiBzA\nY3u04ezY8xDOjJhOrbrXiPcrcq7Ye6jf+CGRtC7nAjluZ0oUDR/bi9a5OssjrKFuL1c+Pu3iAR9b\nOrx73gyJbtJqkspTbgfoiJZmlYwBBgHrgFrAB9yG08e9SFXfSUTIWCnLzXuQTjinW5Hw4X4TNu7K\nmVlVnT1mtPrSUqZQny544tWScGBj0mxDNrLHeaum9L8mzyOLsqWKN3IK8y9zO0RHtNTifhD4rqo2\nXFkUAYpEZDIwF2cnHC95CPgKzi8gTwv7Mmr3D5pSWjl0RqCmx9A8xBe3OcteEg68N9jtDI3tqNkw\ntSqw/4Mrh372iE/SznY7jwHg124H6KiWWtwbVbXJy8VFpFRV/XFLFidluXl/AT7hdo72CKZ3P7p7\naP7mPUOmpddl9fN31sHF9oqEDmwLHH+mxV1u3JDpy6q6Ztjd27qmd7/Q7Swpbhtwbk5hfsTtIB3R\nUos76wzPdY1lkAT6NR4q3LVZ/SsrcmZu3z9wcq9gRmoMLrZXqG5FJZCUhTsQqev9UsW8iTMG31Qy\nqOvZSdOVk4Ie9nrRhpZb3M8Cxar6+GmP3wVcpao3xTlfXJTl5i3Bmd6YlI71PPv9XWddsfdQvwmD\nI2ldbFpZK9UdfXAnRJK+O2Jc72lvTuidP0VEuridJcXsAc7JKcw/6XaQjmqpcA8C/goEgIaByMlA\nJnCjqnpmc83GynLzJgOrcJaodZ0i4cN9x2+qOGvm0arsMSPVlz7c7UxeEw7u2hSsed4rF4QxMGv4\npssH39RfxDfI7Swp5J6cwvzfuR0iFlp7Ac5MTi2NuklVi+OaKgHKcvOeAz7l1vnDvvS6/QOnbKgc\nNiNQ02NYLuLr71aWzqD++HPLNFTpqZkCXdN67rs2567DGb4unvmF42FbgPFenrvdWKs3UuhsynLz\nxuBctt+Wne47xBlcvHTzniHT0+uy+k3AO1vAJTXVSLi+6qHDoAPdztJWPtLqPzLs9tW9Mwd6YtVF\nD/tkTmH+C26HiJWULdwAZbl5jwBfjuc5arP67a7ImbnNGVzsMQGRhP2iSBXhwPvvBk8sjNsu8Ylw\nYb+PlIzueYHtKB8fy3MK8zvVoH6qF5Hv4cwwieli+Md6nr11V07BnkP9/YMiaV1ygaTYyquzCtWt\n9NwOJqd75/A/Zxyoq3hn2oDrR4tIb7fzdCJB4B63Q8RaSre4Acpy8z4B/KUjx1AkcrjvuI0VOQVH\nq3qPGaG+9KSf2dBZqIbq66sergOy3c4SCz0z+u28eugdoTRfelJOa/Sgn+YU5n/P7RCxlvKFG6As\nN+8F4Ma2vMcZXJxcWjlsRn1NjxwbXHRJqH79ytDJJUm9hVpbpUvm8Wty7izrnp491e0sHvc+cF5O\nYX6920FizQo3UJabNxTYTAuttmB6t6rdQy/dtGfI9LS6rP5+G1x0X331H1ZopPpit3PEgV4y8MaS\nnO7nXu52EI9SYGZOYX6J20HiwQp3VFlu3udoYsWw2qy+eypyZn6wf+CUnsGMHn4bXEweqvXH6qvm\nZXLmK3w97ZxeF759Qd8rzrcd5dvskZzC/K+6HSJerHA3Upab9yfglmM9h2+tyCnYc6iff1A4PSvX\n7VymaaHaFW+F6pZ3qtkCTenbZciWK4bc1t0nqbvqYxttAKZ2xi6SBla4GynLzetZcukvF4bTszx1\nIUeqqquavxY9OcntHInQxdft0LU5d+/uktbVVoE8sxpgSk5h/ntteZOI1ADTgKejDw3H2W+3Gjik\nqleKyLk4K6aeizNbpRRntdHjwOPAeThXY1cB16hqTce/nWbyWuH+sHmzi/3ACsA+miYxjdQcrK/+\nXV9SaB9RwRcsGHLriv5Zw2xH+eZ9Oqcw/89tfZOI1Khqj0b3nwIWqurz0ftZOIX6G6r6UvSxmcBB\nnOWtB6jqN6KPjwXKVTVuLX7Pr0sda3PmF5TSCed9djahutVlpFDRBmdH+SV7n8kvq1qxTFU9uTt5\nnP26PUW7lW4F3m4o2gCq+rqqbgSGALsbPb4lnkUbrHA3ac78gmeAR93OYZoXrt+Usjupbzhactkb\n+5/frKqH3M6SRN4AvhXH40/g1EJ7p3sC+LaIvC0iPxGRc+KYA7DCfSZfB5a7HcL8u0j4yC4IpPTC\nTHtrt5+/qPKx+lAk0Ka+3E5qD3BTTmG+K/vgquo6YBTwANAXWC0iefE8pxXuZsyZXxAAZgHvup3F\nfFiobsUOtzMkgxOh6mEv7npk+LHgkVRuYBwArsgpzN8b5/NsAprdvUhVa1T1BVX9EvAM8NF4hrHC\nfQZz5hdUAVfh/NBMkogEttq0uKiwhrotrnx8+o7jG5eqqud3dmmjw8CVbZ1B0k5/AqaLyKyGB0Tk\nGhHxi8glItIn+lgmMA7YGc8wVrhbMGd+wSHgCpz1fI3LIqE970PY1vE4zapDiy5fdejlNap6zO0s\nCVINXJVTmF+aiJOpai1wHfAVEdkqIpuBO3Ba/KOBEhEpxfmEvoYOrn/UEpsO2ErzZhcPA5bh9GUZ\nlwSOv1ASCZXbno3N6J05cNuVQ29PS5O0EW5niaPjOEV7hdtB3GIt7laaM79gN84+lbvczpKqVFUj\noZ1xH7H3sqrAgdF/2/VIn9pQzRq3s8TJSWBWKhdtsMLdJnPmF+zEKd573M6SiiLBHaWgMV07vTMK\nRuqzX6p49IJ9tTs62wJLdcD1OYX5b7gdxG1WuNtozvyCbTh93gfczpJqQnUrqt3O4BWKppXse27G\nhiMlb6lqndt5YiAAfCKnMH+J20GSgRXudpgzv+A94EqcUW2TAKrhoIb3pfTc7fYoq15xyev7nt0e\n0cg+t7N0QAhnnvZit4MkCyvc7RS9NH4aYBdAJEA48N46nIsbTBsdrKsYt7DitxKI1G10O0s7HAau\nzinMf9HtIMnECncHzJlfsBW4CFjkdpbOLly32tbm6IDacM2gv+38zTlH6/e/6XaWNtiIs9JfsdtB\nko0V7g6aM7/gGHA98DO3s3RWqoETGjliy5l2UIRwl1f3PHXp1mPvlKhq2O08LXgBmJZTmG9XyTbB\n5nHH0LzZxTfhLDhjS8LGUKhuzfJQ7bLpbufoTHK6nbt2+sAbRjZc8ZdEFPgh8KOcwnwrTs2wwh1j\n82YXXwD8DTjL7SydRV3V71ajNVPcztHZ9Mzou/PqoXcE03wZY9zOElUDfC6nMP8Ft4MkO+sqibE5\n8wveBSbjLDNpOkgjJ4+gNRPdztEZHQ8eOfvFXY8MPhGsWul2FmAHMN2KdutY4Y6DOfMLDuDM9X7M\n7SxeF6pbswnIcDtHZxXSYI+FlY9NrTixpUTd+/hdjDMImZB1RzoD6yqJs3mzi+8BfoX1e7dLXdWj\nG9C689zOkQrG9LxgxaR+H/GLSPcEnTKCs4fjt91aS9urrHAnwLzZxaNwNhMtcDuLl0TC1XsCx/4w\nBGcDVpMAfTOHvH/F0Nu6JWBH+feAu3IK81N5LfF2s8KdQPNmF98N/ALIdjuLFwRPvFoSDmy0lQAT\nrIuv2+Frc+6q6JLWLR5jCyHg5zizRuK6L2NnZoU7webNLh4K/BZn7rc5g7qjD2+B0Fi3c6QiQUIz\nh9yyfEDWWZfF8LBrgTtzCvPXx/CYKckKt0uic74fBga6nSUZRUIHtgWOP2MbJrhsQu/8N8b1nnZR\ndGeX9qrDmZv9C+vLjg0r3C6aN7u4H/AQcJvbWZJNoObvJZHgB9ZNkgQGdx254bJB/zFExDegHW9/\nA7g7pzD//VjnSmVWuJPAvNnFHwXmYxft/Evd0Qd3QuRst3MYR7f07D3XDLuzOsOX2drdy48D3wEe\ntSsgY88Kd5KYN7u4J/Aj4EtARz6Wel44uGtTsOZ5W8I1yaRJeu1VQ+94t1dmv5aWH3geuDenMN92\ni4oTK9xJZt7s4pHAj4FbSdFpcPXHn1umocpYDoqZGJrS/9qlI3v4LxOR0y/gW4ozJ3uVC7FSihXu\nJDVvdvFEoBC42u0siaQaCddXPXQY1AZtk9jZPcavvqj/rHNFJBvYANxnGx0kjhXuJDdvdnEB8FPg\nYrezJEI4sGVt8MSiSW7nMC3rkzm45CNDb39cRJ7NKcyPuJ0nlVjh9oh5s4uvBn6As+tOp1V/7Ok3\nNXzwUrdzmDP6AKcx8cy9Cxba9D4XWOH2mHmziz8CzAU63frUqqG6+qqHA0Avt7OYJm0FfgL88d4F\nC5N9I4ZOzZXCLSI1qtqj0f07gMmq+mURmQ2cVNX/iT7+qqruiUOGp4CFqvp8E4/PAKqBLOBZVf1h\nrM/fUfNmF18JfAtn0+JOMYgZqlu3IlRbnBJdQh6zDmehtD9ZwU4O6W4HOJ2qzm909w6cfediXrhb\n8E1VfV5EsoDNIvI/qppUWyjNmV/wGvDavNnFY4B7gM8D/dxN1THh+nfcjmBOqQUWAPPvXbAwGdbr\nNo0kXeEWkbk4O2GU42xI8EcRqQWmqWpto9d9AfhPnDnPHwCfVdWT0Rbzseh7BwPfihZhAR7BWaFv\nB61rpWZF/zwRPWc5zieDQyIyGfiFql4ezTwSGAKcC3wDZzDxWmA38DFVDUbfvwCYGT3urar6QVv+\nfk43Z37BB8A3580u/h7wKWA2cElHjukGjdRVa6TaNkxw3xaci8GK7l2w8KjbYUzT3CrcXUVkXaP7\nfYG/N35BtNh+Gfi/qrqmiWO8oKqPA4jIT4C7cAozOAX0UiA3etzngRuBsYAfGARsxtkfsikPiMj3\ngDHAw6p6oBXf02icgjwOeBv4pKp+S0T+CswCXoy+7piqThWR23HWIr6uFcdu0Zz5BfXAM8Az82YX\n+3EK+GfwSH9xuP7dUpyfmUm8IPBXnNb1626HMS1zq3DXquq/WlcNfdxtPMaEaMHuDfQAXmn03Iuq\nGsHp5hgUfewynP7qMLBHRIrPcOyGrpIewBIRma6qLa0bvDjaqi4F0oB/RB8vBUY0et2zjf78dQvH\nbJc58wtKgTnzZhd/G+dCntnABfE4V6yE6tfbRhOJtxP4HfCHexcs3O92GNN6SddV0gZPATeo6vpo\n4b+80XON1/lt3CXSppFYVa0RkaU4LcHlOGsJN1wtlnXay+uj74mISLDRNlARPvz3rM18HXNz5hfU\n4PzH/N282cUX4fSFf5Ika4VrpOYAevJ8t3OkiHrgVZxt9Rbfu2Chzb/2oGQv3MeBns081xPYKyIZ\nOKvr7W7hWMuAe0Tkf3CWUp0J/OlMbxCRdOAiTnXBlAMXAotxCmB73IRzReRNOF0qCTFnfsFKYOW8\n2cWzcb73G3DWBB+aqAzNCdWteg/nE5GJj2PAyzjdIYvvXbDwuMt5TAcle+F+Cpjf1OAk8N/ASpyP\ne6U0X+Ab/BVnYLIUeB8oOcNrG/q4M4ElQMPO0z8E/iAi342euz26iMhKnJb7Le08RrvNmV8QwOlW\nemXe7OIvAVNxivgNOGMCCReu3+zp2TBJag/O+M6LwOv3LlgYcDmPiSG7ACeBGs9KcTtLU+bNLh7L\nqSJ+EQmYHx4JH9kZOPaULd8aG+/hFOoXgVX3Llho/7k7KSvcCZTshbuxebOLhwAfxyniM4nTUrOB\nmkUlkeAW2zChfRRYhfNp8sV7Fyzc4nIekyBWuE2L5s0u7gpMwbnMvuEWk+6NuqMPbYfwqFgcKwWc\nwCnUb+EMlr9974KFVe5GMm6wwm3aJdqt0riQ59HGrpVIaM+WwPH/tc2Am7cLp0A3FOr1dsm5ASvc\nJkbmzS7ug7NyYUMhnwp0P9N7Asf/UhIJ7bRuEkcQeBenQC8Hlt+7YGFLM6VMirLCbeJi3uziNJyr\nVMfjtMbHRf8cA6SrqtZXPbgPdIiLMd2gODOhyqK3zdHbunsXLKw90xuNaWCF2yTUvNnFGcCYSPjw\nOYFjRXk4SwU03HJwrjrtDPYC24FtjW5lwHv3Llh40s1gxvuscJuk8cubrsvAWR5gNM6O9/2it75N\nfN0XyEhgvAhwFDgcvR1p4uvdOMV6uxVnE09WuI1n/fKm63pyqqA3Lurdaf8c9DqaLs5VNi/aJAsr\n3MYY4zG+ll9ijDEmmVjhNsYFIlLj4rk/JSJlIvJva2+LyLki8rKIfBB9zXONlkaOV56hIvJ8y680\nDayrxBgXnL7vaoLP/Q/gflV9/bTHs3AWYfuGqr4UfWwmcFBVNyY+qWmOtbiNSRIicraILBGRDdE/\nh0cf/5SIbBSR9SKyLPrYeBFZJSLroq8/p4nj3SIipdH33h997Ps468vPF5EHTnvLrcDbDUUbQFVf\nV9WNIpIlIk9Gj/dutKAjIneIyAsi8g8R2SoiP48+niYiT0XPXSoiX48+PkZEXot+L2tFZLSIjBCR\njdHnz3Se3zT63haKyOVnOM9SEbk/+nf0vojkN8r1gIisjv693RN9fIiILIv+fW4Ukfzmjp0Mkn1Z\nV2NSyW+A/1HVIhG5E3gYZ5Gv7wNXq+puEekdfe1s4CFV/aOIZHLa/HcRGQrcj7N+/FHgVRG5QVV/\nJCIFNL0l4ASguR2b5wCoql9EcqPHOzf63EScHZbqgS0i8gjOmvfDVHVCNE9D7j8Char612gL3xd9\nbWvO05SJzZwHnAu9porIR4EfAFfibHFYrapTRKQL8JaIvAp8AnhFVX8qImlAtxaO7SprcRuTPKZx\nanOPpzm1B+dbwFPibJDdUKDfBr4rIt8Gzj5trXpwFgVbqqoHVTWEUzA7slnFpdFMqOp7OFd/NhTU\nJaparap1OFeBno0zn32UiDwiItcAx0SkJ04h/Gv0OHWqevp89zOdpyn/dp5GzzWso/8Op7YPvAq4\nXZw9b1fiTB89B1gNfF6cjb/9qnq8hWO7ygq3MclLAVR1NvA9nIuS1olIP1X9E84ORrXAK9FWdGPt\nmce+CaeF3pQzHa/xVoFhnJbuUeB8YClOK/r3rczU3GsabxsI0a0DmznP6bnCnOpdEOArqjoxehup\nqq+q6jKcX2y7gadF5PYWju0qK9zGJI/lwM3Rr28D3gQQkdGqulJVvw8cAs4SkVHAdlV9GGenm/NO\nO9ZKYIaI9I9+9L+FM+/6BE5rf7qIzGp4QESuERE/ztZ/t0UfOxcYDjS7/reI9Ad8qvoXnN2qJqnq\nMaBSRG6IvqaLiJy+SXRz5ykHJoqIT0TOwlnErMnztPA9vgJ8UZwtDxtm0XQXkbOBA6r6OPAHYFI7\njp0w1sdtjDu6iUhlo/u/Ar4KPCEi3wQOAp+PPvdAdPBRcLbSWw/cB3xGRILAPuBHjQ+uqntF5DvA\n69H3vayqfztTIFWtFZHrgAdF5EGcFQs3AF8DHsUZ0CzFaf3eoar1Is02oocBT4pIQ+PwO9E/Pws8\nJiI/ih7/UzjLCTRo7jxvATtwZr1sBNa2cJ7m/B6n22StOOEP4owjXA58M/r3WQPc3o5jJ4xNBzTG\nGI+xrhJjjPEYK9zGGOMxVriNMcZjrHAbY4zHWOE2xhiPscJtjDEeY4XbGGM8xgq3McZ4jBVuY4zx\nGCvcxhjjMVa4jTHGY6xwG2OMx1jhNsYYj7HCbYwxHmOF2xhjPMYKtzHGeIwVbmOM8Rgr3MYY4zFW\nuI0xxmOscBtjjMdY4TbGGI/5/wk/Ux0pLKSDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a3cb3deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fall_data=pd.read_csv(\"multiclassfall.csv\")\n",
    "fall_data.CategoryID.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Near Fall Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a391c4240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eV/k08LDrHPHQo1kbrlkaWfHgPZFd1/01ep4V/vg71v27zmFiUgn8wHWIVBX4\nzh8gXBEeDlQBQ11nORl9G/TQtX+Nrr9go4ayFDsgmWDW/aeFKHBeSXnRatdBUlXgO3/423r/X3Gd\no6sGHtbar/0hsvSBH0UiF76qC6zwJ4d1/2nhP63wd8w6/xbCFeFHgH9wnaMzI/br219YHNkeeouZ\nAv1c5wki6/5T2qvAuSXlRfWug6SyoM/2ae3zeCezTe3siS6Mf1+3f3Fx5N1xH1AoMMZ1niCzmT8p\naz9wtRX+zlnn30q4IjwJWAvkus5yzOk7ddMtSyIHRxygUGxJipRh3X/KUeDjJeVFS1wHSQdW/NsQ\nrghfDvwRx4X2nKroyzc8E5XcI0x3mcO0b8vETyzflX/BPNc5DOCdxXu76xDpwop/O8IV4buAbyX9\ng1X1old09Weejw7s28jpSf980yXW/aeMp4DLSsqLoq6DpAsb82/ft4FZwEXJ+LDsiDZduVJXX/lS\nNK9nhHOS8Zmm+2zsPyXsABZa4e8a6/w7EK4ID8JbF+TMRH1GryY9svD56NqLX9ZJ2Upeoj7HJI51\n/07tAeaVlBdVuQ6Sbqz4dyJcER4JrAAmxfN9c+p1/w1/jr56bpVOzUrTk8vMcTb278QBoKikvOhl\n10HSkRX/GIQrwuPwvgC6vQTy0IP63s1LopvPrNEZAjndT2dSgXX/SXcEuKSkvOgF10HSlRX/GPlT\nQFfAyZ1FO2aP7ixZHHlzwrvMFugd33QmFWyZePXyXflF1v0nXiNweUl50Z9dB0lnVvy7IFwRPhNY\nCgyO9TWT3tbNJU9E9o7eR6HYchoZLSo9GpbOu2+fdf8JFQH+vqS86A+ug6Q7K/5dFK4InwM8SydD\nNmdvjb5609PRpiF1zExOMpMKrPtPKAU+V1Je9IDrIJnAiv9JCFeEC4EltD5Qq6oLNura6/4a7du/\ngbCTcMYp6/4Tphm4vqS86CHXQTKFFf+TFK4Ih4BngPysqEYuW6OrPrkiOqJ3c3xnBZn0Y91/3B0B\nPllSXvSk6yCZxIp/N4QrwuMuXRO97zPPR2f1iDLWdR6TGqz7j6t9eOv1vOg6SKax4t9NVVNCg/HW\nAbJOz/yNdf9xsQO4tKS8qNp1kExks0+6KVRdtQ+4GHjEdRaTOiZu/1MhGn3PdY40tgY452QKv4jU\niUhYRDb4l70issO//hf/OaeJyJMisk1EqkTkdyIyUkT6ichDIlIpIq+JyAsikpHn41jxj4NQdVUD\nsBD4Lt6MBBNwWdrcO//tpbbb18n5HXBBSXnRByf7BqpaqarTVHUa8L/Abf7tj4hIH7wJG/er6kRV\nDQH3A8OBfwLeV9Wwqk4FbgDSXZu2AAAG40lEQVSauv03SkG2sFuchKqrFLi9akpoJfAgtmRD4E3c\n/qfCXWMWvGdj/zFrBL5WUl70Hwn+nE8DK1X1iWN3qOrzACJyE7Czxf2bE5zFGev84yxUXfU0MB1Y\n6TqLccu6/y7ZCcxNQuEHb6e+9e089ivgGyKyUkTuFJGMnb1nxT8BQtVVbwHzgftcZzFu2dh/TJ4A\nppeUF61xHURVNwCnAvcAQ4C1IhJymyoxrPgnSKi6qilUXfUV4Eq81QdNAFn336Fm4BvAFSXlRfuS\n+LmvAzPae1BV61T1MVX9At4Q7qVJS5ZEVvwTLFRd9UfgbLx9gU0AWfffprfwlmP+QUl5UbInSTwM\nnCcixcfuEJGP+jOEzheRwf59vYDTaXEMIJNY8U+CUHXVG8C5wNeBesdxTJJlaXPvsbuWZuyBwy5S\n4KfAGSXlRSucBFCtBy4DbhWRrSKyCbge+ACYACwTkUrgFWAdkJGLyNlJXklWNSU0EfgF3jEBExD+\nWb97kazRrrM4tAX4vKuib05knX+ShaqrtgEXADcDBx3HMUnid/9bXedwpBn4V+AsK/ypwzp/h6qm\nhPLxTi65zHUWk3gB7f43ADfYVoupxzp/h0LVVbtC1VUfB64BdrnOYxIrYN3/IWARMMsKf2qyzj9F\nVE0J9QW+jPc/zADHcUyCBKD7bwZ+DpR2Z3kGk3hW/FNM1ZTQCKAUuBFbfiMjbZ1w9fK3xmbkip9P\nAF+3VTjTgxX/FFU1JRTCO0j2cddZTHxlYPe/Hm9NnqWug5jY2Zh/igpVV1WFqqsux5sZ1N46JCYN\nZdDY/5vAZ/HG9Zc6zmK6yDr/NFE1JVSMdzxgjusspvvSvPvfgver9MGS8qJG12HMybHin2aqpoTm\nAN8kQ9cbCZI0HPt/GbgbeKykvCjqOozpHiv+aapqSugsvF8CnwSyHccxJyGNuv/ngbtLyouedR3E\nxI8V/zRXNSU0AbgNb22S3m7TmK5K4e5f8XbAurukvGi16zAm/qz4Z4iqKaGhwLV4U0Qzcv3xTJSC\n3f+7wK+BX5SUF+1wHcYkjhX/DFQ1JTQXuAn4BNDHcRzTiRTo/qPAM8DPgCdKyouaHWYxSWLFP4NV\nTQkN5vivgTMcxzHt8Lr/H+1FspPd/b+Dt23hL0rKizJyzXrTPiv+AVE1JXQecB3ezmLDHccxrWyd\ncNXyt8ZemIzu/wiwBHgIWFxSXhRJwmeaFGTFP2CqpoSygQV4s4SuBEY4DWSAhHf/h/EK/u+BJ0vK\ni44k4DNMmrHiH2D+F8E8vC+Cq4CRbhMFW5y7/zpgMV7Bf6qkvMh2kDMnsOJvAKiaEsrC+yK4CrgE\nOM1touCJQ/f/LvAX4HG8gn80fulMprHib9pUNSU0DrjIv1wIDHObKBi62P0fBpYBzwLPlpQXvZ64\nZCbTWPE3naqaEhJgGse/DOZgU0gTopPuP4q3ofiz/mWlra1jTpYVf9Nl/sYz5wDn+pdzsF8GcdOi\n+68D1gIrgVXACyXlRfuchjMZw4q/iYuqKaGJQCEwE5gBTAdynIZKL41AJbC+qUe/1Svm3LMeeM2m\nYppEseJvEsI/gDwZ70tgSovLJII9ZNQM7ACqW1w2AhtD1VU2hGOSxoq/SSr/S2E8J34hTMabXTQK\nEHfp4movsBXYzImFfluouqrJZTBjwIq/SSFVU0I98b4A8oAx/p9tXc91lRFvhs07rS5vt74eqq6y\naZYmpVnxDwgRqcM7OPsb/65xwAH/sgf4PFCF16n2wptVcoOqNolIP+DnwJl4nfl+4KOqWpfUv4TP\nPzktBxjQyaU/sf2SUKAeONTZJVRdZYuemYxgxT8gRKROVXNa3H4AWKyqj/q3C/zbU0UkG28q4S9V\n9SER+SYwXFW/4j93MlCjqg1J/msYY+Kkh+sAJvWoakRE1uANsQCMBna2eHyzk2DGmLjJch3ApB4R\n6YM3bfNp/65fAd8QkZUicqeITHKXzhgTD1b8TUsTRGQDUAu8qaobAVR1A3AqcA8wBFgrIrZbmDFp\nzIq/aWm7qk4DJgLniMjlxx5Q1TpVfUxVvwA8CFzqKqQxpvus+JsPUdV3gUXANwFE5HwRGexf7wWc\nTotjAMaY9GPF37Tnj0A/EZkLTACWiUgl8AreNNA/uAxnjOkem+ppjDEBZJ2/McYEkBV/Y4wJICv+\nxhgTQFb8jTEmgKz4G2NMAFnxN8aYALLib4wxAWTF3xhjAsiKvzHGBJAVf2OMCSAr/sYYE0BW/I0x\nJoCs+BtjTABZ8TfGmACy4m+MMQFkxd8YYwLIir8xxgSQFX9jjAkgK/7GGBNAVvyNMSaArPgbY0wA\nWfE3xpgAsuJvjDEBZMXfGGMC6P8AJ+qW82ScjeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a3ca6c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "near_fall_data=pd.read_csv(\"MultiNear.csv\")\n",
    "near_fall_data.CategoryID.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activities of Daily Life (ADL) Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a391df2b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CjwPlbbH8bKKgawcy/zdTC4bv6Sw9XOdpD/k24typXdacH/9xx1V67CDXWdrJxZk401ZE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a391d9438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "near_fall_data=pd.read_csv(\"ADLMulti.csv\")\n",
    "near_fall_data.CategoryID.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acceleration Analysis (Waistometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a39172da0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7GT99LRe8Pp8/dsr4TJem8azZU0T7RrF8cH1/Xvh5PVvySomLsOGwWfhLz2a0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\nXPDNzDLxX8i86ijv2/OVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a3919bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slip_data=pd.read_csv('TRIP1.csv')\n",
    "slip_data.waistAccelerationX.plot(kind='line')\n",
    "slip_data.waistAccelerationY.plot(kind='line')\n",
    "slip_data.waistAccelerationZ.plot(kind='line',colors='R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Magnetic Field Analysis (Waistometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a39108cf8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sZGVL3p+/w0X9VVhoVCWGSjGRAPS1mLznjOzu9tqpIzrTvX4Fl/b7etQhzhSf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\nFEVRGjiqCBRFURo4qggURVEaOP8PWQKeDyRzKNkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a391224a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slip_data.waistMagneticFieldX.plot(kind='line')\n",
    "slip_data.waistMagneticFieldY.plot(kind='line')\n",
    "slip_data.waistMagneticFieldZ.plot(kind='line',colors='R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Angular Velocity Analysis (Waistometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a390c7c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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67VGE3HEHTJzIb7/+ySCH+z/NyiHPNOTdmRv5e04OeXH1ic4+CLk+/kqnT0sv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a3904f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slip_data.waistAngularVelocityX.plot(kind='line')\n",
    "slip_data.waistAngularVelocityY.plot(kind='line')\n",
    "slip_data.waistAngularVelocityZ.plot(kind='line',colors='R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "### Remove Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features in training dataset(before feature selection by Variance Threshold):- 135\n",
      "Number of Features in training dataset(after feature selection by Variance Threshold):- 132\n"
     ]
    }
   ],
   "source": [
    "#Performing Feature Selection on Training Dataset\n",
    "print(\"Number of Features in training dataset(before feature selection by Variance Threshold):-\",X_train.shape[1])\n",
    "X_train_temp = X_train.copy(deep=True)  # Make a deep copy of the Training Data dataframe\n",
    "selector = VarianceThreshold(0.12)\n",
    "selector.fit(X_train_temp)\n",
    "X_res = X_train_temp.loc[:, selector.get_support(indices=False)]\n",
    "X_train=X_res\n",
    "print(\"Number of Features in training dataset(after feature selection by Variance Threshold):-\",X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation between each feature and class variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 132)\n",
      "Find most important features relative to target through correlation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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wxxXjOQ44hNKzWNXFcp+K8Qz7CHBGd2HXlPr96hNZbK+TtKxLIidJGsv09C1o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a3904f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "traini = pd.concat([X_train, Y_train], axis=1, join='inner')\n",
    "\n",
    "# Find most important features relative to target i.e finding correlation of every individual feature i.e independent variable with dependent variable and then sorting them and using the features that have maximum correlation\n",
    "print(\"Find most important features relative to target through correlation\")\n",
    "corr = traini.corr()\n",
    "corr.sort_values([\"class_label\"], ascending = False, inplace = True)\n",
    "\n",
    "#Selecting top-10 features\n",
    "corr.class_label[1:10].plot(kind='bar',colors='RGBY')\n",
    "top_features=corr.class_label[1:10].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 10 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean_lthighMagneticFieldY', 'mean_category', 'mean_waistMagneticFieldY', 'mean_trial', 'mean_sternumMagneticFieldY', 'mean_headAccelerationX', 'mean_waistAccelerationX', 'mean_sternumMagneticFieldX', 'mean_subject']\n"
     ]
    }
   ],
   "source": [
    "features=[]\n",
    "columns=top_features.index\n",
    "for col in columns:\n",
    "    features.append(col)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select same features in Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "Index(['mean_lthighMagneticFieldY', 'mean_category',\n",
      "       'mean_waistMagneticFieldY', 'mean_trial', 'mean_sternumMagneticFieldY',\n",
      "       'mean_headAccelerationX', 'mean_waistAccelerationX',\n",
      "       'mean_sternumMagneticFieldX', 'mean_subject'],\n",
      "      dtype='object')\n",
      "9\n",
      "Index(['mean_lthighMagneticFieldY', 'mean_category',\n",
      "       'mean_waistMagneticFieldY', 'mean_trial', 'mean_sternumMagneticFieldY',\n",
      "       'mean_headAccelerationX', 'mean_waistAccelerationX',\n",
      "       'mean_sternumMagneticFieldX', 'mean_subject'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Selecting Features for training dataset\n",
    "X_train=X_train[features]\n",
    "print(X_train.shape[1])\n",
    "print(X_train.columns)\n",
    "\n",
    "\n",
    "#Selecting Same Features for testing dataset\n",
    "X_test=X_test[X_train.columns]\n",
    "print(X_test.shape[1])\n",
    "print(X_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Models and Comparing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating training and cross-validation dataset\n",
    "X_train,X_CV,Y_train,Y_CV=train_test_split(X_train,Y_train,test_size=0.30)\n",
    "\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "results_sensitivity={}\n",
    "results_specitivity={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 100.0 Specificity 69.2307692308\n",
      "Error Rate:\n",
      "False Positive Rate- 30.7692307692 True Positive Rate- 0.0\n",
      "Confusion matrix, without normalization\n",
      "[[ 9  4]\n",
      " [ 0 11]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.69230769  0.30769231]\n",
      " [ 0.          1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38fc3048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wIrYvnJEHz2eFs4AnI+KYldptD9TWWboCLouIG1faxpnrsI07gMMi4g1JQ4AB\nBctWXlfk2z49IgrDC0nda7hdq4YP36w2jQS+IqkngKRWknoBY4AtJPXI2x2zhtc/Tfbz4lXjNxsA\nn5L1gqoMB4YWjFVtmv+Iwr+Ar0tqKakN2aHi2rQBpktqBgxeadmRkprkNW8JjM23fWreHkm9JK1f\nxHasBtxTsloTETPzHsc9ktbLZ58fEe9JOgl4VNIsYATQbzWrOAO4SdLxZHfZPDUiXpD0XP6V++P5\nuNLWwAt5T20+8K2IeE3SfcAoYDLZIeba/B/wYt7+LVYMv7HAs2T3rzolIhZJuoVsrOm1/OZ6M4HD\nivvbsWL52jczS4oP38wsKQ4lM0uKQ8nMkuJQMrOkOJTMLCkOJTNLikPJzJLiUDKzpPw/mW7TaGGg\nOVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38f70f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Applying Logistic Regression\n",
    "log_reg=LogisticRegression()\n",
    "log_reg=log_reg.fit(X_train, Y_train)\n",
    "log_pred=log_reg.predict(X_CV)\n",
    "score=accuracy_score(Y_CV,log_pred)\n",
    "#print(score)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, log_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Logistic']=sensitivity\n",
    "results_specitivity['Logistic']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,log_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 88.4615384615 Specificity 96.2962962963\n",
      "Error Rate:\n",
      "False Positive Rate- 3.7037037037 True Positive Rate- 11.5384615385\n",
      "Confusion matrix, without normalization\n",
      "[[26  1]\n",
      " [ 3 23]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96296296  0.03703704]\n",
      " [ 0.11538462  0.88461538]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38f1f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38f2b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Decision Tree\n",
    "decision_reg = DecisionTreeClassifier(criterion='gini',\n",
    "                                            max_depth=10, max_leaf_nodes=23, splitter='best')\n",
    "decision_reg=decision_reg.fit(X_train, Y_train)\n",
    "decision_pred=decision_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, decision_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['DT']=sensitivity\n",
    "results_specitivity['DT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,decision_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 90.4761904762 Specificity 79.3103448276\n",
      "Error Rate:\n",
      "False Positive Rate- 20.6896551724 True Positive Rate- 9.52380952381\n",
      "Confusion matrix, without normalization\n",
      "[[23  6]\n",
      " [ 2 19]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.79310345  0.20689655]\n",
      " [ 0.0952381   0.9047619 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38fcc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a38fe2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gaussian Naive Bayes\n",
    "naivebay_reg = GaussianNB()\n",
    "naivebay_reg=naivebay_reg.fit(X_train, Y_train)\n",
    "naivebay_pred=naivebay_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, naivebay_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['NB']=sensitivity\n",
    "results_specitivity['NB']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,naivebay_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 90.9090909091 Specificity 73.9130434783\n",
      "Error Rate\n",
      "False Positive Rate- 26.0869565217 True Positive Rate- 9.09090909091\n",
      "Confusion matrix, without normalization\n",
      "[[17  6]\n",
      " [ 2 20]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.73913043  0.26086957]\n",
      " [ 0.09090909  0.90909091]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31cca550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31c89ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying AdaBoost Classifier\n",
    "ada_reg = AdaBoostClassifier(random_state=2, n_estimators=500)\n",
    "ada_reg=ada_reg.fit(X_train, Y_train)\n",
    "ada_pred=ada_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, ada_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Adaboost']=sensitivity\n",
    "results_specitivity['Adaboost']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,ada_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 100.0 Specificity 79.1666666667\n",
      "Error Rate:\n",
      "False Positive Rate- 20.8333333333 True Positive Rate- 0.0\n",
      "Confusion matrix, without normalization\n",
      "[[19  5]\n",
      " [ 0 24]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.79166667  0.20833333]\n",
      " [ 0.          1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31b55e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZzgBmVsfM2gMTgR3NrF1od8JG3v8Oyc+Ll4zfbAssJukFlRgBnJoyVtUy/IjC\n/wG/MLNCM6tHsqu4KfWAWWZWCxhYat6xZlYj1LwT8HVY99mhPWbW3sy2SWM9UgHqKUmlcfe5ocfx\nrJltFSb/wd0nmdkZwKtmNg8YBXQpYxEXAg+a2Wkkd9k8293fN7PR4ZD762FcaVfg/dBTWwKc5O6f\nmtlzwFhgGsku5qb8EfggtP+C9cPva+BdkvtXneXuK8zsYZKxpk/DzfXmAkel97cj6dK1byISFe2+\niUhUFEoiEhWFkohERaEkIlFRKIlIVBRKIhIVhZKIREWhJCJR+X8/VLGzwqEIDQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31b5c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gradient Boosting Classifier\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01}\n",
    "gb_reg = ensemble.GradientBoostingClassifier(**params)\n",
    "gb_reg=gb_reg.fit(X_train, Y_train)\n",
    "gb_pred=gb_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, gb_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['GBR']=sensitivity\n",
    "results_specitivity['GBR']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,gb_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 84.6153846154 Specificity 82.7586206897\n",
      "Error Rate:\n",
      "False Positive Rate- 17.2413793103 True Positive Rate- 15.3846153846\n",
      "Confusion matrix, without normalization\n",
      "[[24  5]\n",
      " [ 4 22]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.82758621  0.17241379]\n",
      " [ 0.15384615  0.84615385]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31b1e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31b701d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Random Forest Classifier\n",
    "rf_reg = RandomForestClassifier(random_state=1)\n",
    "rf_reg=rf_reg.fit(X_train, Y_train)\n",
    "rf_pred=rf_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, rf_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['RForest']=sensitivity\n",
    "results_specitivity['RForest']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,rf_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Majority Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 88.4615384615 Specificity 86.6666666667\n",
      "Error Rate:\n",
      "False Positive Rate- 13.3333333333 True Positive Rate- 11.5384615385\n",
      "Confusion matrix, without normalization\n",
      "[[26  4]\n",
      " [ 3 23]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.86666667  0.13333333]\n",
      " [ 0.11538462  0.88461538]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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7/OueDBk6tOjSLLeidiUn3fAcU2YsoMt67XnmV3vz2Euz+c0fX+Hs2yYDcOy3tuXk/Qdx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31d054a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zw+kV8+9J+jDdhz2BnjnjTS3Sbb+bx7YsTw4ly9dXEdE7d0YaPF/kzgKejogj\n1mjXG6iuq3QF/C4ibl9jG2duwDbuBg6IiKmSjgEG5yxbc12Rbvu0iMgNLyR1qOJ2rRI+fLPqNB74\ngaRtACQ1ldQZeBvYWlKntN0R63j9v0h+Xrxi/OZ7wOckvaAKY4Hjcsaq2qQ/ovAccKCkJpI2IjlU\nXJ+NgHmSGgBHrbFsmKR6ac0dgXfSbZ+StkdSZ0nN8tiOVYF7SlZtImJB2uN4QFKjdPavI+JdSScB\nYyQtBJ4HeqxlFWcAd0g6nuQum6dExEuSXkhPuf8zHVfqBryU9tSWAj+OiMmSHgKmADNJDjHX5yLg\n5bT966wefu8A/ya5f9XJEfG1pD+TjDVNTm+utwA4IL//OpYvf/fNzDLFh29mlikOJTPLFIeSmWWK\nQ8nMMsWhZGaZ4lAys0xxKJlZpjiUzCxT/j+WCakU/Rb1DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31b55da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying Majority Voting Concept\n",
    "majority_class = VotingClassifier(estimators=[('dr', decision_reg),\n",
    "                                    ('gnb', gb_reg),('naive_bayes',naivebay_reg)],\n",
    "                                    voting='hard')\n",
    "majority_class = majority_class.fit(X_train, Y_train)\n",
    "majority_pred=majority_class.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, majority_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['MVoting']=sensitivity\n",
    "results_specitivity['MVoting']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,majority_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D8y7nkZ2Ic6bnj8qqJL8AnAFc1NdyYgb2JJPcluRdSb7d585J/fSpJH/eT/9oku/O0oGa\nWVUti3/AfSOmfR54dT/874HP9cNfADb2w78+/Vi6XtJN/fAf0PV4oQuXowbnj2j/BuCzwOH9+FOX\n6DnfDfzEwPhVwLqlfn9mqh94MnAb3Y1rbwM29/M20/V6dvXP6bJFWP8rgT/sh78B/AzwK8Cf0122\n+1PAPcBZw+8pcCnwywOv8cf64V8c2obe2Q+/GNg1x/TPA8/rh59EdzXaeuALE3zOb6HbIxo1bz3d\nT3/soutl/hXw5H7ea4AP9sOr+jZHLtY23b/+nwY29OOrgfv79e4CLp7jtdwMXAcc1Y9vAn63H348\n3R7tCcBbgXcMrPPowToWsD2fQvf7WEf2df7o/QN+EvgeD2fELcDJA+/9q/vhwaz65PT2NzxO97l5\ncz/8RuDj/fAHgd/uhzfQ3e2/Yr7PZ1n33IGfBy7rhy8Fnj8w/dP98GXDD+pdA/xOkt8Cjq+q++dY\n12nAR6r7bRyq6lF32D5GDpVeOwBV9X+BS+hCZ9j0YZmnA09MMulDJxvpfsiO/v+NdOF8eVU9VFV3\nAF8daP+idD9JfSNdkDxrYN7lAFV1NfDkdMeKn0+33VFVXwWeluQps0z/OvBfkrwFOGZ6W1pMSS5O\nd75mRz9p+rDMccB/A9470PzsJHuAfcAHqruEedKOSrILuAt4Kt0X7bTBwzLTh2tmei0Btg58bl8G\nvKpf9jfpfrtqDd2hp9emO9fz7Kr6+4Mpvqp2030RbeTRl3//b2AP3Q8jPgf4YVXd1M+eKavm8if9\n/9f166V/7BX9Ov+MBe7xLvdwHzb2dZtVdRndLtH9wPYkL57jIZnP8hdDkp8GHgLuXMo6FuC/0h0a\neeKomVX1Q+DP6IJ3IpI8jS6gP57kNuDtwNnM8D4mORL4EF2v6dnAx+h6Zz8qc7hsZv7NpJHTq+rd\nwH+g20u8NjOc5D1Ie+j2UKZX+ibgJcDUiLZbeeRr/qnqzu+8AHhfkp9chPru77/Qj6fbY37THO1n\n+12qfxhq9+aBL4cTqupL/ZfxL9LtJV6aZBLnpbYCv8cjD8lMmz40c84M86eNmyU/6P9/iIfvO5pI\nB2+5h/s3ePgY16/RnTgDuBb41/3wyN5gH5T7qur36d6sU4C/pzsOOcqXgF9Pf/ImyVMPuvp5SDIF\nfIRu1/mQuvmg38u5ki7gH6U//vsLwF9PcLVnAZdU1fFVtbrvqX6H/g7p/hjtM4AX9e2ng/z7/bH6\n4RPpZ/e1Ph+4t6ruBa6m2+5Ish74fr+nMnJ6khOr6saqeg/dYYOTmH2bW4ivAkcmecPAtCfM0Pb5\njHjNq+oaut7lf5pgXcPruJdub+5tSX5slqYzvcbDtgNvmF5WkmcmeWKS44E7q+pjdHfKT3/x/XCO\n9c7mE8CFVXXjiHmfpbvw4Wwe3muEmbNqIe//XwK/CpDkZcCPz/PxwPIK9yeku9Ro+t95dBvHa5Ps\nBv4dD2+MvwGcl+RbdCeF7h2xvLOBm/rduJPoguAu4Ov9iZeLhtp/HPgbYHeSG5jgFQ6zmD7pswf4\nMt0XzLseg/UuhvfR/RLeoN/sX/+b6HolH5rg+jYCfzo07bN0x0X/F3Aj8GHgfwJU1T10vfUb6a42\n2TH02LuTfIPuC3b6S2ozsK7f/t7Nw7+fNNP03+i3rRvo9hi/COwGHuwPnRz0CdX+i/8VwAuTfKf/\nDPwR8Ft9k+mTtzfQfWbeOsOi3kP32ZrkF89wrdcDNzD7lUybGf1aDvs4cDPw7XQXQXyUh89p7Epy\nPV2H7wN9+y10n+X5nlClqvZX1QdmmHcPXefy/1TVdwZmzZRVVwBvT3eC/cQxS3gX8LIk36b7I0nf\no/uSmJdD8g7VJE+g2/2r/jjuxqoa/gMiknTISfJ44KHqftfr54EP94e65mUh148uBz8LfLDf3b+H\n7uy0JLVgFXBluhumHgBev5CFHJI9d0nS7JbTMXdJ0oQY7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfp/\nDi4VPZ3Yr6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31c1dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_sensitivity)), list(results_sensitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_sensitivity)), list(results_sensitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I/It+eB1wxcC8K3h0J+Ls2fmjsirJLwGnAxf2tRyfgS3JJLcneU+Sb/e5c0I/fSbJn/fT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\nkhpkuEtSg/4/PGwVWSVjbWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3a31bd73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_specitivity)), list(results_specitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_specitivity)), list(results_specitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
